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  • Intelligent Segmentation and Measurement Model for Asphalt Road Cracks Based on Modified Mask R-CNN Algorithm
  • Abstract Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, cracks have been paid more attention, since cracks often cause major engineering and personnel safety incidents. Current manual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model based on the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The model proposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposal network (RPN), a region of interest (RoI) Align…
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  • Deep Learning-Based Surrogate Model for Flight Load Analysis
  • Abstract Flight load computations (FLC) are generally expensive and time-consuming. This paper studies deep learning (DL)-based surrogate models of FLC to provide a reliable basis for the strength design of aircraft structures. We mainly analyze the influence of Mach number, overload, angle of attack, elevator deflection, altitude, and other factors on the loads of key monitoring components, based on which input and output variables are set. The data used to train and validate the DL surrogate models are derived using aircraft flight load simulation results based on wind tunnel test data. According to the FLC features, a deep neural network (DNN)…
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  • Steganalysis of Low Embedding Rate CNV-QIM in Speech
  • Abstract To address the difficulty of detecting low embedding rate and high-concealment CNV-QIM (complementary neighbor vertices-quantization index modulation) steganography in low bit-rate speech codec, the code-word correlation model based on a BiLSTM (bi-directional long short-term memory) neural network is built to obtain the correlation features of the LPC codewords in speech codec in this paper. Then, softmax is used to classify and effectively detect low embedding rate CNV-QIM steganography in VoIP streams. The experimental results show that for speech steganography of short samples with low embedding rate, the BiLSTM method in this paper has a superior detection accuracy than state-of-the-art methods…
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  • Computational Analysis of Airflow in Upper Airway under Light and Heavy Breathing Conditions for a Realistic Patient Having Obstructive Sleep Apnea
  • Abstract Background: Obstructive sleep apnea is a sleeping disorder that has troubled a sizeable population. There is an active area of research on obstructive sleep apnea that intends to better understand airflow behaviors and therefore treat patients more effectively. This paper aims to investigate the airflow characteristics of the upper airway in an obstructive sleep apnea (OSA) patient under light and heavy breathing conditions by using Turbulent Kinetic Energy (TKE), an accurate method in expressing the flow concentration mechanisms of sleeping disorders. It is important to visualize the concentration of flow in the upper airway in order to identify the severity…
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  • A Contemporary Review on Drought Modeling Using Machine Learning Approaches
  • Abstract Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Its beginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughts in the last few decades. Predicting future droughts is vital for framing drought management plans to sustain natural resources. The data-driven modelling for forecasting the metrological time series prediction is becoming more powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques have demonstrated success in the drought prediction process and are becoming popular to predict the weather, especially the…
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  • Stability Reliability of the Lateral Vibration of Footbridges Based on the IEVIE-SA Method
  • Abstract Research on the lateral vibrational stability of footbridges has attracted increasing attention in recent years. However, this stability contains a series of complex mechanisms, such as nonlinear vibration, random excitation, and random stability. The Lyapunov method is regarded as an effective tool for analyzing random vibrational stability; however, it is a qualitative method and can only provide a binary judgment for stability. This study proposes a new method, IEVIE–SA, which combines the energy method based on the comparison between the input energy and the variation of intrinsic energy (IEVIE) and the stochastic averaging (SA) method. The improved Nakamura model was…
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  • An Improved Algorithm for the Detection of Fastening Targets Based on Machine Vision
  • Abstract Object detection plays an important role in the sorting process of mechanical fasteners. Although object detection has been studied for many years, it has always been an industrial problem. Edge-based model matching is only suitable for a small range of illumination changes, and the matching accuracy is low. The optical flow method and the difference method are sensitive to noise and light, and camshift tracking is less effective in complex backgrounds. In this paper, an improved target detection method based on YOLOv3-tiny is proposed. The redundant regression box generated by the prediction network is filtered by soft nonmaximum suppression (NMS)…
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  • An Efficient Meshless Method for Hyperbolic Telegraph Equations in (1 + 1) Dimensions
  • Abstract Numerical solutions of the second-order one-dimensional hyperbolic telegraph equations are presented using the radial basis functions. The purpose of this paper is to propose a simple novel direct meshless scheme for solving hyperbolic telegraph equations. This is fulfilled by considering time variable as normal space variable. Under this scheme, there is no need to remove time-dependent variable during the whole solution process. Since the numerical solution accuracy depends on the condition of coefficient matrix derived from the radial basis function method. We propose a simple shifted domain method, which can avoid the full-coefficient interpolation matrix easily. Numerical experiments performed with…
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  • High Order of Accuracy for Poisson Equation Obtained by Grouping of Repeated Richardson Extrapolation with Fourth Order Schemes
  • Abstract In this article, we improve the order of precision of the two-dimensional Poisson equation by combining extrapolation techniques with high order schemes. The high order solutions obtained traditionally generate non-sparse matrices and the calculation time is very high. We can obtain sparse matrices by applying compact schemes. In this article, we compare compact and exponential finite difference schemes of fourth order. The numerical solutions are calculated in quadruple precision (Real * 16 or extended precision) in FORTRAN language, and iteratively obtained until reaching the round-off error magnitude around 1.0E −32. This procedure is performed to ensure that there is no…
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  • MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks
  • Abstract The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automatically segment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancing tumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, it is very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantages of DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks. We used dense blocks in the encoder part and residual blocks in the decoder part. The number…
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